Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes
Bibliographic record
Abstract
–Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here, we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location parameters of the marginal generalized extreme value (GEV) distributions of the observed extremes, to a practical and better-understood method. Marginal approaches based on a weighted sum of marginal GEV score equations are promising for no need to specify the dependence structure. The computational efficiency makes them feasible in handling multiple forcings simultaneously. The method under working independence is recommended because it produces robust results where there are errors-in-variables. Our analyses show human influences on temperature extremes at the subcontinental scale. Compared with previous studies, we detected human influences in a slightly smaller number of regions. This is possibly due to the under-coverage of the confidence intervals in existing works, suggesting the need for careful examinations of the properties of the statistical methods in practice. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".